{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9043a2d8-1aff-416b-8508-a60509c14b3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T01:36:20.589162Z",
     "iopub.status.busy": "2022-03-21T01:36:20.588684Z",
     "iopub.status.idle": "2022-03-21T01:36:22.798967Z",
     "shell.execute_reply": "2022-03-21T01:36:22.798031Z",
     "shell.execute_reply.started": "2022-03-21T01:36:20.589120Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.7.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.19.5)\n",
      "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2019.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib) (3.0.7)\n",
      "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (41.4.0)\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d973c6bf-ac62-4f75-b3e0-851d93039e51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T01:58:10.712784Z",
     "iopub.status.busy": "2022-03-21T01:58:10.711824Z",
     "iopub.status.idle": "2022-03-21T01:58:10.750714Z",
     "shell.execute_reply": "2022-03-21T01:58:10.749886Z",
     "shell.execute_reply.started": "2022-03-21T01:58:10.712707Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>china</th>\n",
       "      <th>usa</th>\n",
       "      <th>king</th>\n",
       "      <th>japan</th>\n",
       "      <th>russia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1920.000000</td>\n",
       "      <td>3906.327801</td>\n",
       "      <td>19019.580913</td>\n",
       "      <td>14975.016598</td>\n",
       "      <td>11803.473029</td>\n",
       "      <td>9165.580913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>69.714896</td>\n",
       "      <td>7897.060024</td>\n",
       "      <td>20966.605026</td>\n",
       "      <td>14186.285966</td>\n",
       "      <td>15780.652526</td>\n",
       "      <td>10130.108008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1800.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>3043.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1860.000000</td>\n",
       "      <td>735.000000</td>\n",
       "      <td>3354.000000</td>\n",
       "      <td>4926.000000</td>\n",
       "      <td>1116.000000</td>\n",
       "      <td>1831.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1920.000000</td>\n",
       "      <td>785.000000</td>\n",
       "      <td>8134.000000</td>\n",
       "      <td>7973.000000</td>\n",
       "      <td>2620.000000</td>\n",
       "      <td>3415.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1980.000000</td>\n",
       "      <td>1078.000000</td>\n",
       "      <td>29523.000000</td>\n",
       "      <td>20733.000000</td>\n",
       "      <td>21614.000000</td>\n",
       "      <td>15604.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2040.000000</td>\n",
       "      <td>34823.000000</td>\n",
       "      <td>79957.000000</td>\n",
       "      <td>56528.000000</td>\n",
       "      <td>55158.000000</td>\n",
       "      <td>39276.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              time         china           usa          king         japan  \\\n",
       "count   241.000000    241.000000    241.000000    241.000000    241.000000   \n",
       "mean   1920.000000   3906.327801  19019.580913  14975.016598  11803.473029   \n",
       "std      69.714896   7897.060024  20966.605026  14186.285966  15780.652526   \n",
       "min    1800.000000    560.000000   1965.000000   3043.000000   1009.000000   \n",
       "25%    1860.000000    735.000000   3354.000000   4926.000000   1116.000000   \n",
       "50%    1920.000000    785.000000   8134.000000   7973.000000   2620.000000   \n",
       "75%    1980.000000   1078.000000  29523.000000  20733.000000  21614.000000   \n",
       "max    2040.000000  34823.000000  79957.000000  56528.000000  55158.000000   \n",
       "\n",
       "             russia  \n",
       "count    241.000000  \n",
       "mean    9165.580913  \n",
       "std    10130.108008  \n",
       "min     1365.000000  \n",
       "25%     1831.000000  \n",
       "50%     3415.000000  \n",
       "75%    15604.000000  \n",
       "max    39276.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as plt\n",
    "import pandas as pd\n",
    "data=pd.read_csv(\"./line_animation.csv\")\n",
    "\n",
    "year=data['time']\n",
    "china=data['china']\n",
    "usa=data['usa']\n",
    "japan=data['japan']\n",
    "russia=data['russia']\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ff44811-68d5-447c-8442-22ec2b1a69da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84a2ffc1-8aa0-4535-9c6f-d018615cc2d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:02:15.367188Z",
     "iopub.status.busy": "2022-03-21T02:02:15.366591Z",
     "iopub.status.idle": "2022-03-21T02:02:16.106486Z",
     "shell.execute_reply": "2022-03-21T02:02:16.105619Z",
     "shell.execute_reply.started": "2022-03-21T02:02:15.367147Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2064: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcf90c5f590>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "## 中文显示问题\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "#创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(5,5)\n",
    "fig2.suptitle(\"各国GDP\",fontproperties=myfont)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)# 给第一个图副值titile\n",
    "#我们单独给第一个图的x轴y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color='red',label='GDP' )\n",
    "#定义第三个字图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='blue')\n",
    "# 加入图例\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "748072f3-7ee7-4d3a-a14e-47c583f2b16f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:25:23.059337Z",
     "iopub.status.busy": "2022-03-21T02:25:23.058261Z",
     "iopub.status.idle": "2022-03-21T02:25:23.452119Z",
     "shell.execute_reply": "2022-03-21T02:25:23.451118Z",
     "shell.execute_reply.started": "2022-03-21T02:25:23.059270Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'年')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots()\n",
    "fig.set_size_inches(10,10)\n",
    "axes.set_xlim(1800,2040)\n",
    "axes.set_ylim(0,40000)\n",
    "axes.plot(year,china,color=\"red\")\n",
    "axes.set_ylabel(\"人均GDP美元\",fontproperties=myfont)\n",
    "axes.set_xlabel(\"年\",fontproperties=myfont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "097f9d7c-9a8e-4116-b528-55e9e6d1380c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:48:52.590546Z",
     "iopub.status.busy": "2022-03-21T03:48:52.589133Z",
     "iopub.status.idle": "2022-03-21T03:48:53.953412Z",
     "shell.execute_reply": "2022-03-21T03:48:53.952092Z",
     "shell.execute_reply.started": "2022-03-21T03:48:52.590495Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'aistudio/tutu.jpeg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_858/3518280116.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m     \u001b[0mfig2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, **kwargs)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2261\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2262\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2263\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2264\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2265\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_jpg\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    586\u001b[0m                 \u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dpi'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mbackground\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'jpeg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m         \u001b[0mprint_jpeg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprint_jpg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2167\u001b[0m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2168\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2169\u001b[0;31m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2171\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'aistudio/tutu.jpeg'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(15,15)\n",
    "\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)\n",
    "ax1.set_xlim(1800,2040)\n",
    "ax1.set_ylim(0,40000)\n",
    "ax1.set_ylabel(\"人均GDP美元\",fontproperties=myfont)\n",
    "ax1.set_xlabel(\"年\",fontproperties=myfont)\n",
    "ax1.annotate('1979年改革开放',xy=(1979,1064),xytext=(1950,5000),arrowprops=dict(facecolor='r',shrink=0.05),fontproperties=myfont)\n",
    "ax1.plot(year,china,color=\"red\")\n",
    "\n",
    "\n",
    "\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim(1800,2040)\n",
    "ax2.set_ylim(0,40000)\n",
    "ax2.set_ylabel(\"人均GDP美元\",fontproperties=myfont)\n",
    "ax2.set_xlabel(\"年\",fontproperties=myfont)\n",
    "# 加入图例\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)\n",
    "fig2.subplots_adjust(left=None,hspace=0.5,wspace=0.3)\n",
    "ax2.plot(year,usa,color=\"blue\")\n",
    "\n",
    "ax3=axes2[1,1]\n",
    "ax3.set_title(\"日本\",fontproperties=myfont)\n",
    "ax3.set_xlim(1800,2040)\n",
    "ax3.set_ylim(0,40000)\n",
    "ax3.set_ylabel(\"人均GDP美元\",fontproperties=myfont)\n",
    "ax3.set_xlabel(\"年\",fontproperties=myfont)\n",
    "ax3.plot(year,japan,color=\"yellow\")\n",
    "\n",
    "ax4=axes2[0,1]\n",
    "ax4.set_title(\"俄罗斯\",fontproperties=myfont)\n",
    "ax4.set_xlim(1800,2040)\n",
    "ax4.set_ylim(0,40000)\n",
    "ax4.set_ylabel(\"人均GDP美元\",fontproperties=myfont)\n",
    "ax4.set_xlabel(\"年\",fontproperties=myfont)\n",
    "ax4.plot(year,russia,color=\"green\")\n",
    "\n",
    "# 导出文件到本地保存\n",
    "\n",
    "gfile='aistudio/tutu.jpeg'\n",
    "if os.path.exists(gfile):\n",
    "    os.remove(gfile)\n",
    "else:\n",
    "    fig2.savefig(gfile)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "038ff550-7ec1-44c4-98b7-f9cd6211ac85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:40:24.178122Z",
     "iopub.status.busy": "2022-03-21T03:40:24.177140Z",
     "iopub.status.idle": "2022-03-21T03:40:24.183573Z",
     "shell.execute_reply": "2022-03-21T03:40:24.182742Z",
     "shell.execute_reply.started": "2022-03-21T03:40:24.178078Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/aistudio\n"
     ]
    }
   ],
   "source": [
    "cd aistudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "debf1796-e20d-4d4d-bf61-c88167709e52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:35:18.267196Z",
     "iopub.status.busy": "2022-03-21T03:35:18.266339Z",
     "iopub.status.idle": "2022-03-21T03:35:18.558120Z",
     "shell.execute_reply": "2022-03-21T03:35:18.556997Z",
     "shell.execute_reply.started": "2022-03-21T03:35:18.267130Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1950,5000,'1979年改革开放')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出中国人均GDP折线图，并标注重大事件时间\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "fig,axes=plt.subplots()\n",
    "axes.plot(year,china)\n",
    "\n",
    "axes.annotate('1979年改革开放',xy=(1979,1064),xytext=(1950,5000),arrowprops=dict(facecolor='r',shrink=0.05),fontproperties=myfont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "68577d76-33ec-413e-af72-7133c3af3a8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:34:41.762591Z",
     "iopub.status.busy": "2022-03-21T02:34:41.761360Z",
     "iopub.status.idle": "2022-03-21T02:34:44.153158Z",
     "shell.execute_reply": "2022-03-21T02:34:44.152266Z",
     "shell.execute_reply.started": "2022-03-21T02:34:41.762542Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (1.19.5)\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0b1ccec8-1722-40b7-82a7-13082fb80523",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:35:15.595659Z",
     "iopub.status.busy": "2022-03-21T02:35:15.594782Z",
     "iopub.status.idle": "2022-03-21T02:35:15.933246Z",
     "shell.execute_reply": "2022-03-21T02:35:15.932328Z",
     "shell.execute_reply.started": "2022-03-21T02:35:15.595620Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制sin函数，并通过调整spines来得到标准坐标轴\n",
    "import numpy as np \n",
    "x=np.linspace(-2*np.pi,2*np.pi,200)\n",
    "y=np.sin(x)\n",
    "fig3,axes3=plt.subplots()\n",
    "axes3.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fe385001-f72c-455d-a1a7-b5248d136d6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:57:40.320770Z",
     "iopub.status.busy": "2022-03-21T02:57:40.319562Z",
     "iopub.status.idle": "2022-03-21T02:57:40.754375Z",
     "shell.execute_reply": "2022-03-21T02:57:40.753201Z",
     "shell.execute_reply.started": "2022-03-21T02:57:40.320714Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.FancyArrow at 0x7fcf8fb6e590>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 通过设置spines，将上面的sin图进行修饰\n",
    "import numpy as np \n",
    "x=np.linspace(-2*np.pi,2*np.pi,200)\n",
    "y=np.sin(x)\n",
    "fig3,axes3=plt.subplots()\n",
    "fig3.set_size_inches(11,11)\n",
    "axes3.set_xlim(-7,7)\n",
    "axes3.set_ylim(-1,1)\n",
    "axes3.plot(x,y)\n",
    "axes3.spines['top'].set_visible(False)\n",
    "axes3.spines['right'].set_visible(False)\n",
    "axes3.spines['left'].set_position(('data',0))\n",
    "axes3.spines['bottom'].set_position(('data',0))\n",
    "# 为图像添加网格\n",
    "axes3.grid(color='gray',linestyle='--',alpha=0.2)\n",
    "# 为x轴，y轴绘制箭头\n",
    "# 绘制x轴箭头\n",
    "axes3.arrow(-7,0,13.8,0,width=0,shape='full',head_width=0.05,head_length=0.2)\n",
    "# 绘制y轴箭头\n",
    "axes3.arrow(0,-1,0,1.97,width=0,shape='full',head_width=0.25,head_length=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b8803488-00af-441d-92b0-111da35ebc23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:12:04.224674Z",
     "iopub.status.busy": "2022-03-21T03:12:04.224174Z",
     "iopub.status.idle": "2022-03-21T03:12:04.415997Z",
     "shell.execute_reply": "2022-03-21T03:12:04.415100Z",
     "shell.execute_reply.started": "2022-03-21T03:12:04.224635Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 结合箭头与网格，我们可以很直观第表达线性代数中向量的概念\n",
    " \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 我们使用numpy中的数组来表示向量\n",
    "v = np.array([2,1])\n",
    "\n",
    "# 绘制图像，展现向量的空间表达.\n",
    " \n",
    "fig,axes=plt.subplots()\n",
    "axes.set_xlim(0,3)\n",
    "axes.set_ylim(0,3)\n",
    "axes.grid()\n",
    "axes.arrow(0,0,v[0],v[1],head_length=0.1,head_width=0.1,color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f9921818-db39-44fc-ac29-51434bc6a3db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:27:01.001672Z",
     "iopub.status.busy": "2022-03-21T03:27:01.001126Z",
     "iopub.status.idle": "2022-03-21T03:27:02.360167Z",
     "shell.execute_reply": "2022-03-21T03:27:02.359436Z",
     "shell.execute_reply.started": "2022-03-21T03:27:01.001632Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2064: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import os\n",
    "## 中文显示问题\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "#创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(12,12)\n",
    "fig2.suptitle(\"各国GDP\",fontproperties=myfont)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)# 给第一个图副值titile\n",
    "#我们单独给第一个图的x轴y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color='red',label='GDP' )\n",
    "#加入箭头指示说明文字\n",
    "ax1.annotate('1979年改革开放',xy=(1979,1064),xytext=(1920,10000),arrowprops=dict(facecolor='r',shrink=0.05),fontproperties=myfont)\n",
    "#定义第三个字图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='blue')\n",
    "# 加入图例\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)\n",
    "fig2.subplots_adjust(left=None,hspace=0.5,wspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb3aaa74-1a20-4a3a-9e7e-db49b6edbf0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:28:57.775292Z",
     "iopub.status.busy": "2022-03-21T03:28:57.774341Z",
     "iopub.status.idle": "2022-03-21T03:28:57.780510Z",
     "shell.execute_reply": "2022-03-21T03:28:57.779735Z",
     "shell.execute_reply.started": "2022-03-21T03:28:57.775248Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 20] Not a directory: '信息分析数据可视化.ipynb'\n",
      "/home/aistudio\n"
     ]
    }
   ],
   "source": [
    "cd 信息分析数据可视化.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1041bc8-caeb-4dfd-949b-6072188788c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d613fa20-38bc-4c80-b1ef-9f87b9a4bf6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:30:48.728654Z",
     "iopub.status.busy": "2022-03-28T02:30:48.727820Z",
     "iopub.status.idle": "2022-03-28T02:30:48.732934Z",
     "shell.execute_reply": "2022-03-28T02:30:48.732357Z",
     "shell.execute_reply.started": "2022-03-28T02:30:48.728622Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成二维随机数组： [[0.56513995 0.72347909 0.56124052 0.90340762 0.94683011]\n",
      " [0.8203878  0.81146776 0.91580315 0.14211411 0.04372075]\n",
      " [0.70823605 0.13144101 0.06516178 0.06218593 0.09427153]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "n=np.random.rand(3,5)\n",
    "print(\"生成二维随机数组：\",n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83dbad9a-f69f-41ac-ab3f-8a82168584d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:48:44.847390Z",
     "iopub.status.busy": "2022-03-28T02:48:44.846637Z",
     "iopub.status.idle": "2022-03-28T02:48:44.995436Z",
     "shell.execute_reply": "2022-03-28T02:48:44.994816Z",
     "shell.execute_reply.started": "2022-03-28T02:48:44.847353Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48\n",
      " 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72\n",
      " 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96\n",
      " 97 98 99]\n",
      "[  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.  14.\n",
      "  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.  28.\n",
      "  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.  42.\n",
      "  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.  56.\n",
      "  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.  70.\n",
      "  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.  84.\n",
      "  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.  98.\n",
      "  99. 100.]\n",
      "[                  1                   2                   4\n",
      "                   8                  16                  32\n",
      "                  64                 128                 256\n",
      "                 512                1024                2048\n",
      "                4096                8192               16384\n",
      "               32768               65536              131072\n",
      "              262144              524288             1048576\n",
      "             2097152             4194304             8388608\n",
      "            16777216            33554432            67108864\n",
      "           134217728           268435456           536870912\n",
      "          1073741824          2147483648          4294967296\n",
      "          8589934592         17179869184         34359738368\n",
      "         68719476736        137438953472        274877906944\n",
      "        549755813888       1099511627776       2199023255552\n",
      "       4398046511104       8796093022208      17592186044416\n",
      "      35184372088832      70368744177664     140737488355328\n",
      "     281474976710656     562949953421312    1125899906842624\n",
      "    2251799813685248    4503599627370496    9007199254740992\n",
      "   18014398509481984   36028797018963968   72057594037927936\n",
      "  144115188075855872  288230376151711744  576460752303423488\n",
      " 1152921504606846976 2305843009213693952 4611686018427387904\n",
      " 9223372036854775808]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#从数值范围创建数组\n",
    "import numpy as np \n",
    "a1=np.arange(1,100,1)# 注意arange是左闭右开区间[start,end)\n",
    "print(a1)\n",
    "a2=np.linspace(1,100,100)#注意linspace是左闭右闭区间[start,end]\n",
    "print(a2)\n",
    "a3=np.logspace(0,63,64,base=2,dtype='uint64')#注意需要用uint64数据类型，否则会溢出\n",
    "print(a3)\n",
    "import matplotlib.pyplot as plt \n",
    "fig,axes=plt.subplots()\n",
    " \n",
    "axes.plot(a3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5513d3a-e4d6-41fa-bc01-7854a24a0430",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:49:47.533011Z",
     "iopub.status.busy": "2022-03-28T02:49:47.532614Z",
     "iopub.status.idle": "2022-03-28T02:49:47.997414Z",
     "shell.execute_reply": "2022-03-28T02:49:47.996770Z",
     "shell.execute_reply.started": "2022-03-28T02:49:47.532971Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成二维随机数组： [[0.28315989 0.11229972]\n",
      " [0.65415564 0.50280926]\n",
      " [0.66860576 0.88871733]\n",
      " [0.15322793 0.98247747]\n",
      " [0.55661567 0.89532531]]\n",
      "[-0.38581992 -0.70370346  0.17873022 -0.77615386 -2.23782754 -0.33425967\n",
      " -0.26069747  1.63916741  0.98627937 -0.09327616 -0.49874158 -1.44575943\n",
      "  1.46618399 -1.4897118   1.60891274  1.45803905 -0.9634265   0.4324966\n",
      "  0.16481119 -1.59583331  1.15317945  0.38969236 -1.07889721 -0.36616454\n",
      "  0.68675126 -0.78200842  0.78295914 -2.01558099  1.90276919  0.12070493\n",
      " -1.04900466  0.02040548 -0.24875245  0.00697582 -0.92487369 -0.32725335\n",
      " -1.08742971 -0.65164007 -0.20713306 -0.938471    0.59441109 -0.24892291\n",
      " -0.56318448 -0.96227227 -1.05667175  1.91403738  0.8882815  -2.4602225\n",
      " -0.23010224  0.18350278  1.41266386 -1.00082091  1.7318478   0.84030976\n",
      "  0.07606503 -2.07387816 -2.34490279 -0.7146063   2.21700261 -1.13870438\n",
      "  0.06471726 -0.35721445  0.03077239 -0.27345107 -0.84324847  0.65109562\n",
      "  0.45850523  1.56451947  0.02657948  1.67556175  0.54918687  1.19984554\n",
      " -0.18028017  1.13855571  0.79232321 -0.25168938 -0.02612268  0.1684667\n",
      " -0.35940018 -2.35537845  1.55792673  1.05333751 -0.39944526 -0.02789214\n",
      " -1.91450148 -1.16059218 -0.4025162  -1.25342451 -0.49297543  1.31604718\n",
      "  1.57151243 -0.11009752 -1.78579007  0.09485765  1.07373765 -1.57339583\n",
      "  0.94201358  0.59493948  1.66317165  1.61892846]\n",
      "生成1-10（不包括10）的10个随机整数： [39 77 70 83  4 62 78 86 97 86 67 83 29 88  1 35 89 78  5 68 82 72 81 47\n",
      " 95 65 84 68 27 20 47 33 40 68 92 71 26  9 23 62 82 20 22 14 44 72 81 63\n",
      " 35  2 95 96 41 53 69 80 12 45 35 64 84 13 88 73 65 72 19 75 83 16 80 84\n",
      " 17 77 67 58 54 24 92 18 32 13 84 48  3 63 27 15 44 78 57 95 31 88 44 61\n",
      " 18 66 49  5]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa3b0d83810>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 随机数组\n",
    "import numpy as np \n",
    "n=np.random.rand(5,2)\n",
    "print(\"生成二维随机数组：\",n)\n",
    "n1=np.random.randn(100)\n",
    "print(n1)\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "fig,axes=plt.subplots(2,2)\n",
    "ax1=axes[0,0]\n",
    "ax1.hist(n1)\n",
    "ax1.grid()\n",
    "n2=np.random.randint(1,100,100)\n",
    "print(\"生成1-10（不包括10）的10个随机整数：\",n2)\n",
    "\n",
    "n3=np.random.normal(0,10,1000)\n",
    "ax2=axes[0,1]\n",
    "ax2.hist(n3)\n",
    "ax2.grid()\n",
    "ax3=axes[1,0]\n",
    "ax3.plot(n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a7014c1-b35c-4755-ae68-a51bafa4bb0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-04T02:04:32.924967Z",
     "iopub.status.busy": "2022-04-04T02:04:32.924621Z",
     "iopub.status.idle": "2022-04-04T02:04:39.511789Z",
     "shell.execute_reply": "2022-04-04T02:04:39.511087Z",
     "shell.execute_reply.started": "2022-04-04T02:04:32.924934Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机生成1-15数组\r\n",
    "import numpy as np \r\n",
    "np.arange(1,16)\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41faecd8-b3ba-42ff-9006-3900170bb2bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-04T02:37:06.647360Z",
     "iopub.status.busy": "2022-04-04T02:37:06.646788Z",
     "iopub.status.idle": "2022-04-04T02:37:08.191001Z",
     "shell.execute_reply": "2022-04-04T02:37:08.190093Z",
     "shell.execute_reply.started": "2022-04-04T02:37:06.647311Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分班成绩排序： [[30 34 35 37 38 39 39 41 41 41 42 43 43 45 45 45 46 47 48 48 52 53 55 57\n",
      "  58 58 58 59 60 60 61 63 63 64 66 66 70 74 76 79 80 81 84 84 84 87 92 94\n",
      "  95 99]\n",
      " [30 30 33 36 41 41 42 42 44 45 46 46 47 47 47 48 51 52 52 53 55 57 59 60\n",
      "  61 62 63 67 67 68 70 70 71 72 73 75 78 78 80 80 81 83 87 87 92 92 92 94\n",
      "  96 97]\n",
      " [31 32 33 34 35 36 37 38 39 39 39 43 43 45 48 48 52 55 57 58 58 60 60 60\n",
      "  62 62 63 64 67 69 70 71 72 73 74 74 75 76 78 80 82 82 85 88 92 92 93 95\n",
      "  98 99]]\n",
      "将多维成绩数组变更为一维数组，并排序： [30 30 30 31 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 39 39 39 41 41\n",
      " 41 41 41 42 42 42 43 43 43 43 44 45 45 45 45 45 46 46 46 47 47 47 47 48\n",
      " 48 48 48 48 51 52 52 52 52 53 53 55 55 55 57 57 57 58 58 58 58 58 59 59\n",
      " 60 60 60 60 60 60 61 61 62 62 62 63 63 63 63 64 64 66 66 67 67 67 68 69\n",
      " 70 70 70 70 71 71 72 72 73 73 74 74 74 75 75 76 76 78 78 78 79 80 80 80\n",
      " 80 81 81 82 82 83 84 84 84 85 87 87 87 88 92 92 92 92 92 92 93 94 94 95\n",
      " 95 96 97 98 99 99]\n",
      "三个班总的成绩平均值为： 61.43333333333333 标准差为： 19.03730256335936 方差为： 362.4188888888889 最高分为： 99 最低分为： 30 中位数为： 60.0\n",
      "三个班总的成绩平均值为(四舍五入）： 61.0 标准差为(向上取整）： 20.0 方差为（向下取整）： 362.0 最高分为： 99 最低分为： 30 中位数为： 60.0\n",
      "每一个班的最高成绩： [99 97 99]\n",
      "每一个班的平均成绩： [59.18 62.8  62.32]\n"
     ]
    }
   ],
   "source": [
    "\r\n",
    "\r\n",
    "#一元通用函数计算\r\n",
    "#虚拟三个班，60个学生的成绩，作为运算对象\r\n",
    "import numpy as np \r\n",
    "np.random.seed(10)\r\n",
    "stu=np.random.randint(30,100,150).reshape(3,50)\r\n",
    "print(\"分班成绩排序：\",np.sort(stu))\r\n",
    "stu_all=stu.flatten()\r\n",
    "print(\"将多维成绩数组变更为一维数组，并排序：\",np.sort(stu_all))\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "plt.style.use(\"ggplot\")\r\n",
    "fig,axes=plt.subplots(2,2)\r\n",
    "fig.set_size_inches(12,12)\r\n",
    "ax1=axes[0,0]\r\n",
    "ax1.hist(stu_all)\r\n",
    "ax2=axes[0,1]\r\n",
    "ax2.boxplot(stu_all)\r\n",
    "def descStu(arr):\r\n",
    "    avg=np.mean(arr)\r\n",
    "    std=np.std(arr)\r\n",
    "    var=np.var(arr)\r\n",
    "    max_s=np.max(arr)\r\n",
    "    min_s=np.min(arr)\r\n",
    "    med_s=np.median(arr)\r\n",
    "    return avg,std,var,max_s,min_s,med_s\r\n",
    "#计算三个班的总成绩平均值，标准差，方差，最高分，最低分，中位数\r\n",
    "avg_s,std_s,var_s,max_s,min_s,med_s=descStu(stu)\r\n",
    "\r\n",
    "print(\"三个班总的成绩平均值为：\",avg_s,\"标准差为：\",std_s,\"方差为：\",var_s,\"最高分为：\",max_s,\"最低分为：\",min_s,\"中位数为：\",med_s)\r\n",
    "print(\"三个班总的成绩平均值为(四舍五入）：\",np.rint(avg_s),\"标准差为(向上取整）：\",np.ceil(std_s),\"方差为（向下取整）：\",np.floor(var_s),\"最高分为：\",max_s,\"最低分为：\",min_s,\"中位数为：\",med_s)\r\n",
    "print(\"每一个班的最高成绩：\",stu.max(axis=1))#实现对每一行的数据进行聚合\r\n",
    "print(\"每一个班的平均成绩：\",stu.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d1ff4e9a-f5f7-43fc-aa25-f4270da0fb64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-04T02:53:50.403539Z",
     "iopub.status.busy": "2022-04-04T02:53:50.402487Z",
     "iopub.status.idle": "2022-04-04T02:53:51.232979Z",
     "shell.execute_reply": "2022-04-04T02:53:51.232054Z",
     "shell.execute_reply.started": "2022-04-04T02:53:50.403493Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分班成绩排序： [[30 34 35 36 37 38 39 39 41 41 41 41 42 42 43 43 45 45 45 45 46 47 48 48\n",
      "  52 52 53 55 57 58 58 58 59 60 60 61 63 63 64 66 66 70 72 74 76 79 80 80\n",
      "  81 84 84 84 87 87 87 92 92 94 95 99]\n",
      " [30 30 33 33 34 37 38 39 39 41 42 43 44 46 46 47 47 47 48 51 52 52 53 55\n",
      "  55 57 58 58 59 60 60 61 62 62 63 63 67 67 68 69 70 70 71 71 73 73 75 78\n",
      "  78 80 81 82 83 92 92 92 94 96 97 98]]\n",
      "将多维成绩数组变更为一维数组，并排序： [30 30 30 33 33 34 34 35 36 37 37 38 38 39 39 39 39 41 41 41 41 41 42 42\n",
      " 42 43 43 43 44 45 45 45 45 46 46 46 47 47 47 47 48 48 48 51 52 52 52 52\n",
      " 53 53 55 55 55 57 57 58 58 58 58 58 59 59 60 60 60 60 61 61 62 62 63 63\n",
      " 63 63 64 66 66 67 67 68 69 70 70 70 71 71 72 73 73 74 75 76 78 78 79 80\n",
      " 80 80 81 81 82 83 84 84 84 87 87 87 92 92 92 92 92 94 94 95 96 97 98 99]\n",
      "2个班总的成绩平均值为： 60.458333333333336 标准差为： 18.6872932913131 方差为： 349.2149305555556 最高分为： 99 最低分为： 30 中位数为： 58.5\n",
      "2个班总的成绩平均值为(四舍五入）： 60.0 标准差为(向上取整）： 19.0 方差为（向下取整）： 349.0 最高分为： 99 最低分为： 30 中位数为： 58.5\n",
      "每一个班的最高成绩： [99 98]\n",
      "每一个班的平均成绩： [59.88333333 61.03333333]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\r\n",
    "np.random.seed(10)\r\n",
    "stu=np.random.randint(30,100,120).reshape(2,60)\r\n",
    "print(\"分班成绩排序：\",np.sort(stu))\r\n",
    "stu_all=stu.flatten()\r\n",
    "print(\"将多维成绩数组变更为一维数组，并排序：\",np.sort(stu_all))\r\n",
    "\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "plt.style.use(\"ggplot\")\r\n",
    "fig,axes=plt.subplots(2,2)\r\n",
    "fig.set_size_inches(12,12)\r\n",
    "ax1=axes[0,0]\r\n",
    "ax1.hist(stu_all)\r\n",
    "ax2=axes[0,1]\r\n",
    "ax2.boxplot(stu_all)\r\n",
    "def descStu(arr):\r\n",
    "    avg=np.mean(arr)\r\n",
    "    std=np.std(arr)\r\n",
    "    var=np.var(arr)\r\n",
    "    max_s=np.max(arr)\r\n",
    "    min_s=np.min(arr)\r\n",
    "    med_s=np.median(arr)\r\n",
    "    return avg,std,var,max_s,min_s,med_s\r\n",
    "#计算2个班的总成绩平均值，标准差，方差，最高分，最低分，中位数\r\n",
    "avg_s,std_s,var_s,max_s,min_s,med_s=descStu(stu)\r\n",
    "print(\"2个班总的成绩平均值为：\",avg_s,\"标准差为：\",std_s,\"方差为：\",var_s,\"最高分为：\",max_s,\"最低分为：\",min_s,\"中位数为：\",med_s)\r\n",
    "print(\"2个班总的成绩平均值为(四舍五入）：\",np.rint(avg_s),\"标准差为(向上取整）：\",np.ceil(std_s),\"方差为（向下取整）：\",np.floor(var_s),\"最高分为：\",max_s,\"最低分为：\",min_s,\"中位数为：\",med_s)\r\n",
    "print(\"每一个班的最高成绩：\",stu.max(axis=1))#实现对每一行的数据进行聚合\r\n",
    "print(\"每一个班的平均成绩：\",stu.mean(axis=1))\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78839c51-dc7a-477b-83c3-25ac9ac8e6ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
